" lines in HTML files). That may be because this is the only method of the 3 that has a *concept* of "junk" . Example, comparing two strings, and considering blanks to be "junk": >>> s = SequenceMatcher(lambda x: x == " ", ... "private Thread currentThread;", ... "private volatile Thread currentThread;") >>> .ratio() returns a float in [0, 1], measuring the "similarity" of the sequences. As a rule of thumb, a .ratio() value over 0.6 means the sequences are close matches: >>> print(round(s.ratio(), 2)) 0.87 >>> If you're only interested in where the sequences match, .get_matching_blocks() is handy: >>> for block in s.get_matching_blocks(): ... print("a[%d] and b[%d] match for %d elements" % block) a[0] and b[0] match for 8 elements a[8] and b[17] match for 21 elements a[29] and b[38] match for 0 elements Note that the last tuple returned by .get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0. If you want to know how to change the first sequence into the second, use .get_opcodes(): >>> for opcode in s.get_opcodes(): ... print("%6s a[%d:%d] b[%d:%d]" % opcode) equal a[0:8] b[0:8] insert a[8:8] b[8:17] equal a[8:29] b[17:38] See the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work. Timing: Basic R-O is cubic time worst case and quadratic time expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear. """ def __init__(self, isjunk=None, a='', b='', autojunk=True): """Construct a SequenceMatcher. Optional arg isjunk is None (the default), or a one-argument function that takes a sequence element and returns true iff the element is junk. None is equivalent to passing "lambda x: 0", i.e. no elements are considered to be junk. For example, pass lambda x: x in " \\t" if you're comparing lines as sequences of characters, and don't want to synch up on blanks or hard tabs. Optional arg a is the first of two sequences to be compared. By default, an empty string. The elements of a must be hashable. See also .set_seqs() and .set_seq1(). Optional arg b is the second of two sequences to be compared. By default, an empty string. The elements of b must be hashable. See also .set_seqs() and .set_seq2(). Optional arg autojunk should be set to False to disable the "automatic junk heuristic" that treats popular elements as junk (see module documentation for more information). """ # Members: # a # first sequence # b # second sequence; differences are computed as "what do # we need to do to 'a' to change it into 'b'?" # b2j # for x in b, b2j[x] is a list of the indices (into b) # at which x appears; junk and popular elements do not appear # fullbcount # for x in b, fullbcount[x] == the number of times x # appears in b; only materialized if really needed (used # only for computing quick_ratio()) # matching_blocks # a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k]; # ascending & non-overlapping in i and in j; terminated by # a dummy (len(a), len(b), 0) sentinel # opcodes # a list of (tag, i1, i2, j1, j2) tuples, where tag is # one of # 'replace' a[i1:i2] should be replaced by b[j1:j2] # 'delete' a[i1:i2] should be deleted # 'insert' b[j1:j2] should be inserted # 'equal' a[i1:i2] == b[j1:j2] # isjunk # a user-supplied function taking a sequence element and # returning true iff the element is "junk" -- this has # subtle but helpful effects on the algorithm, which I'll # get around to writing up someday . # DON'T USE! Only __chain_b uses this. Use "in self.bjunk". # bjunk # the items in b for which isjunk is True. # bpopular # nonjunk items in b treated as junk by the heuristic (if used). self.isjunk = isjunk self.a = self.b = None self.autojunk = autojunk self.set_seqs(a, b) def set_seqs(self, a, b): """Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 """ self.set_seq1(a) self.set_seq2(b) def set_seq1(self, a): """Set the first sequence to be compared. The second sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq1("bcde") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq2(). """ if a is self.a: return self.a = a self.matching_blocks = self.opcodes = None def set_seq2(self, b): """Set the second sequence to be compared. The first sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("abcd") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq1(). """ if b is self.b: return self.b = b self.matching_blocks = self.opcodes = None self.fullbcount = None self.__chain_b() # For each element x in b, set b2j[x] to a list of the indices in # b where x appears; the indices are in increasing order; note that # the number of times x appears in b is len(b2j[x]) ... # when self.isjunk is defined, junk elements don't show up in this # map at all, which stops the central find_longest_match method # from starting any matching block at a junk element ... # b2j also does not contain entries for "popular" elements, meaning # elements that account for more than 1 + 1% of the total elements, and # when the sequence is reasonably large (>= 200 elements); this can # be viewed as an adaptive notion of semi-junk, and yields an enormous # speedup when, e.g., comparing program files with hundreds of # instances of "return NULL;" ... # note that this is only called when b changes; so for cross-product # kinds of matches, it's best to call set_seq2 once, then set_seq1 # repeatedly def __chain_b(self): # Because isjunk is a user-defined (not C) function, and we test # for junk a LOT, it's important to minimize the number of calls. # Before the tricks described here, __chain_b was by far the most # time-consuming routine in the whole module! If anyone sees # Jim Roskind, thank him again for profile.py -- I never would # have guessed that. # The first trick is to build b2j ignoring the possibility # of junk. I.e., we don't call isjunk at all yet. Throwing # out the junk later is much cheaper than building b2j "right" # from the start. b = self.b self.b2j = b2j = {} for i, elt in enumerate(b): indices = b2j.setdefault(elt, []) indices.append(i) # Purge junk elements self.bjunk = junk = set() isjunk = self.isjunk if isjunk: for elt in b2j.keys(): if isjunk(elt): junk.add(elt) for elt in junk: # separate loop avoids separate list of keys del b2j[elt] # Purge popular elements that are not junk self.bpopular = popular = set() n = len(b) if self.autojunk and n >= 200: ntest = n // 100 + 1 for elt, idxs in b2j.items(): if len(idxs) > ntest: popular.add(elt) for elt in popular: # ditto; as fast for 1% deletion del b2j[elt] def find_longest_match(self, alo=0, ahi=None, blo=0, bhi=None): """Find longest matching block in a[alo:ahi] and b[blo:bhi]. By default it will find the longest match in the entirety of a and b. If isjunk is not defined: Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where alo >> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=1, b=0, size=4) If no blocks match, return (alo, blo, 0). >>> s = SequenceMatcher(None, "ab", "c") >>> s.find_longest_match(0, 2, 0, 1) Match(a=0, b=0, size=0) """ # CAUTION: stripping common prefix or suffix would be incorrect. # E.g., # ab # acab # Longest matching block is "ab", but if common prefix is # stripped, it's "a" (tied with "b"). UNIX(tm) diff does so # strip, so ends up claiming that ab is changed to acab by # inserting "ca" in the middle. That's minimal but unintuitive: # "it's obvious" that someone inserted "ac" at the front. # Windiff ends up at the same place as diff, but by pairing up # the unique 'b's and then matching the first two 'a's. a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__ if ahi is None: ahi = len(a) if bhi is None: bhi = len(b) besti, bestj, bestsize = alo, blo, 0 # find longest junk-free match # during an iteration of the loop, j2len[j] = length of longest # junk-free match ending with a[i-1] and b[j] j2len = {} nothing = [] for i in range(alo, ahi): # look at all instances of a[i] in b; note that because # b2j has no junk keys, the loop is skipped if a[i] is junk j2lenget = j2len.get newj2len = {} for j in b2j.get(a[i], nothing): # a[i] matches b[j] if j < blo: continue if j >= bhi: break k = newj2len[j] = j2lenget(j-1, 0) + 1 if k > bestsize: besti, bestj, bestsize = i-k+1, j-k+1, k j2len = newj2len # Extend the best by non-junk elements on each end. In particular, # "popular" non-junk elements aren't in b2j, which greatly speeds # the inner loop above, but also means "the best" match so far # doesn't contain any junk *or* popular non-junk elements. while besti > alo and bestj > blo and \ not isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ not isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize += 1 # Now that we have a wholly interesting match (albeit possibly # empty!), we may as well suck up the matching junk on each # side of it too. Can't think of a good reason not to, and it # saves post-processing the (possibly considerable) expense of # figuring out what to do with it. In the case of an empty # interesting match, this is clearly the right thing to do, # because no other kind of match is possible in the regions. while besti > alo and bestj > blo and \ isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize = bestsize + 1 return Match(besti, bestj, bestsize) def get_matching_blocks(self): """Return list of triples describing matching subsequences. Each triple is of the form (i, j, n), and means that a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i and in j. New in Python 2.5, it's also guaranteed that if (i, j, n) and (i', j', n') are adjacent triples in the list, and the second is not the last triple in the list, then i+n != i' or j+n != j'. IOW, adjacent triples never describe adjacent equal blocks. The last triple is a dummy, (len(a), len(b), 0), and is the only triple with n==0. >>> s = SequenceMatcher(None, "abxcd", "abcd") >>> list(s.get_matching_blocks()) [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)] """ if self.matching_blocks is not None: return self.matching_blocks la, lb = len(self.a), len(self.b) # This is most naturally expressed as a recursive algorithm, but # at least one user bumped into extreme use cases that exceeded # the recursion limit on their box. So, now we maintain a list # ('queue`) of blocks we still need to look at, and append partial # results to `matching_blocks` in a loop; the matches are sorted # at the end. queue = [(0, la, 0, lb)] matching_blocks = [] while queue: alo, ahi, blo, bhi = queue.pop() i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi) # a[alo:i] vs b[blo:j] unknown # a[i:i+k] same as b[j:j+k] # a[i+k:ahi] vs b[j+k:bhi] unknown if k: # if k is 0, there was no matching block matching_blocks.append(x) if alo < i and blo < j: queue.append((alo, i, blo, j)) if i+k < ahi and j+k < bhi: queue.append((i+k, ahi, j+k, bhi)) matching_blocks.sort() # It's possible that we have adjacent equal blocks in the # matching_blocks list now. Starting with 2.5, this code was added # to collapse them. i1 = j1 = k1 = 0 non_adjacent = [] for i2, j2, k2 in matching_blocks: # Is this block adjacent to i1, j1, k1? if i1 + k1 == i2 and j1 + k1 == j2: # Yes, so collapse them -- this just increases the length of # the first block by the length of the second, and the first # block so lengthened remains the block to compare against. k1 += k2 else: # Not adjacent. Remember the first block (k1==0 means it's # the dummy we started with), and make the second block the # new block to compare against. if k1: non_adjacent.append((i1, j1, k1)) i1, j1, k1 = i2, j2, k2 if k1: non_adjacent.append((i1, j1, k1)) non_adjacent.append( (la, lb, 0) ) self.matching_blocks = list(map(Match._make, non_adjacent)) return self.matching_blocks def get_opcodes(self): """Return list of 5-tuples describing how to turn a into b. Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the tuple preceding it, and likewise for j1 == the previous j2. The tags are strings, with these meanings: 'replace': a[i1:i2] should be replaced by b[j1:j2] 'delete': a[i1:i2] should be deleted. Note that j1==j2 in this case. 'insert': b[j1:j2] should be inserted at a[i1:i1]. Note that i1==i2 in this case. 'equal': a[i1:i2] == b[j1:j2] >>> a = "qabxcd" >>> b = "abycdf" >>> s = SequenceMatcher(None, a, b) >>> for tag, i1, i2, j1, j2 in s.get_opcodes(): ... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))) delete a[0:1] (q) b[0:0] () equal a[1:3] (ab) b[0:2] (ab) replace a[3:4] (x) b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f) """ if self.opcodes is not None: return self.opcodes i = j = 0 self.opcodes = answer = [] for ai, bj, size in self.get_matching_blocks(): # invariant: we've pumped out correct diffs to change # a[:i] into b[:j], and the next matching block is # a[ai:ai+size] == b[bj:bj+size]. So we need to pump # out a diff to change a[i:ai] into b[j:bj], pump out # the matching block, and move (i,j) beyond the match tag = '' if i < ai and j < bj: tag = 'replace' elif i < ai: tag = 'delete' elif j < bj: tag = 'insert' if tag: answer.append( (tag, i, ai, j, bj) ) i, j = ai+size, bj+size # the list of matching blocks is terminated by a # sentinel with size 0 if size: answer.append( ('equal', ai, i, bj, j) ) return answer def get_grouped_opcodes(self, n=3): """ Isolate change clusters by eliminating ranges with no changes. Return a generator of groups with up to n lines of context. Each group is in the same format as returned by get_opcodes(). >>> from pprint import pprint >>> a = list(map(str, range(1,40))) >>> b = a[:] >>> b[8:8] = ['i'] # Make an insertion >>> b[20] += 'x' # Make a replacement >>> b[23:28] = [] # Make a deletion >>> b[30] += 'y' # Make another replacement >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes())) [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)], [('equal', 16, 19, 17, 20), ('replace', 19, 20, 20, 21), ('equal', 20, 22, 21, 23), ('delete', 22, 27, 23, 23), ('equal', 27, 30, 23, 26)], [('equal', 31, 34, 27, 30), ('replace', 34, 35, 30, 31), ('equal', 35, 38, 31, 34)]] """ codes = self.get_opcodes() if not codes: codes = [("equal", 0, 1, 0, 1)] # Fixup leading and trailing groups if they show no changes. if codes[0][0] == 'equal': tag, i1, i2, j1, j2 = codes[0] codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2 if codes[-1][0] == 'equal': tag, i1, i2, j1, j2 = codes[-1] codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n) nn = n + n group = [] for tag, i1, i2, j1, j2 in codes: # End the current group and start a new one whenever # there is a large range with no changes. if tag == 'equal' and i2-i1 > nn: group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n))) yield group group = [] i1, j1 = max(i1, i2-n), max(j1, j2-n) group.append((tag, i1, i2, j1 ,j2)) if group and not (len(group)==1 and group[0][0] == 'equal'): yield group def ratio(self): """Return a measure of the sequences' similarity (float in [0,1]). Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common. .ratio() is expensive to compute if you haven't already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0 """ matches = sum(triple[-1] for triple in self.get_matching_blocks()) return _calculate_ratio(matches, len(self.a) + len(self.b)) def quick_ratio(self): """Return an upper bound on ratio() relatively quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute. """ # viewing a and b as multisets, set matches to the cardinality # of their intersection; this counts the number of matches # without regard to order, so is clearly an upper bound if self.fullbcount is None: self.fullbcount = fullbcount = {} for elt in self.b: fullbcount[elt] = fullbcount.get(elt, 0) + 1 fullbcount = self.fullbcount # avail[x] is the number of times x appears in 'b' less the # number of times we've seen it in 'a' so far ... kinda avail = {} availhas, matches = avail.__contains__, 0 for elt in self.a: if availhas(elt): numb = avail[elt] else: numb = fullbcount.get(elt, 0) avail[elt] = numb - 1 if numb > 0: matches = matches + 1 return _calculate_ratio(matches, len(self.a) + len(self.b)) def real_quick_ratio(self): """Return an upper bound on ratio() very quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute than either .ratio() or .quick_ratio(). """ la, lb = len(self.a), len(self.b) # can't have more matches than the number of elements in the # shorter sequence return _calculate_ratio(min(la, lb), la + lb) __class_getitem__ = classmethod(GenericAlias) def get_close_matches(word, possibilities, n=3, cutoff=0.6): """Use SequenceMatcher to return list of the best "good enough" matches. word is a sequence for which close matches are desired (typically a string). possibilities is a list of sequences against which to match word (typically a list of strings). Optional arg n (default 3) is the maximum number of close matches to return. n must be > 0. Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities that don't score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first. >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"]) ['apple', 'ape'] >>> import keyword as _keyword >>> get_close_matches("wheel", _keyword.kwlist) ['while'] >>> get_close_matches("Apple", _keyword.kwlist) [] >>> get_close_matches("accept", _keyword.kwlist) ['except'] """ if not n > 0: raise ValueError("n must be > 0: %r" % (n,)) if not 0.0 = cutoff and \ s.ratio() >= cutoff: result.append((s.ratio(), x)) # Move the best scorers to head of list result = _nlargest(n, result) # Strip scores for the best n matches return [x for score, x in result] def _keep_original_ws(s, tag_s): """Replace whitespace with the original whitespace characters in `s`""" return ''.join( c if tag_c == " " and c.isspace() else tag_c for c, tag_c in zip(s, tag_s) ) class Differ: r""" Differ is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. Each line of a Differ delta begins with a two-letter code: '- ' line unique to sequence 1 '+ ' line unique to sequence 2 ' ' line common to both sequences '? ' line not present in either input sequence Lines beginning with '? ' attempt to guide the eye to intraline differences, and were not present in either input sequence. These lines can be confusing if the sequences contain tab characters. Note that Differ makes no claim to produce a *minimal* diff. To the contrary, minimal diffs are often counter-intuitive, because they synch up anywhere possible, sometimes accidental matches 100 pages apart. Restricting synch points to contiguous matches preserves some notion of locality, at the occasional cost of producing a longer diff. Example: Comparing two texts. First we set up the texts, sequences of individual single-line strings ending with newlines (such sequences can also be obtained from the `readlines()` method of file-like objects): >>> text1 = ''' 1. Beautiful is better than ugly. ... 2. Explicit is better than implicit. ... 3. Simple is better than complex. ... 4. Complex is better than complicated. ... '''.splitlines(keepends=True) >>> len(text1) 4 >>> text1[0][-1] '\n' >>> text2 = ''' 1. Beautiful is better than ugly. ... 3. Simple is better than complex. ... 4. Complicated is better than complex. ... 5. Flat is better than nested. ... '''.splitlines(keepends=True) Next we instantiate a Differ object: >>> d = Differ() Note that when instantiating a Differ object we may pass functions to filter out line and character 'junk'. See Differ.__init__ for details. Finally, we compare the two: >>> result = list(d.compare(text1, text2)) 'result' is a list of strings, so let's pretty-print it: >>> from pprint import pprint as _pprint >>> _pprint(result) [' 1. Beautiful is better than ugly.\n', '- 2. Explicit is better than implicit.\n', '- 3. Simple is better than complex.\n', '+ 3. Simple is better than complex.\n', '? ++\n', '- 4. Complex is better than complicated.\n', '? ^ ---- ^\n', '+ 4. Complicated is better than complex.\n', '? ++++ ^ ^\n', '+ 5. Flat is better than nested.\n'] As a single multi-line string it looks like this: >>> print(''.join(result), end="") 1. Beautiful is better than ugly. - 2. Explicit is better than implicit. - 3. Simple is better than complex. + 3. Simple is better than complex. ? ++ - 4. Complex is better than complicated. ? ^ ---- ^ + 4. Complicated is better than complex. ? ++++ ^ ^ + 5. Flat is better than nested. """ def __init__(self, linejunk=None, charjunk=None): """ Construct a text differencer, with optional filters. The two optional keyword parameters are for filter functions: - `linejunk`: A function that should accept a single string argument, and return true iff the string is junk. The module-level function `IS_LINE_JUNK` may be used to filter out lines without visible characters, except for at most one splat ('#'). It is recommended to leave linejunk None; the underlying SequenceMatcher class has an adaptive notion of "noise" lines that's better than any static definition the author has ever been able to craft. - `charjunk`: A function that should accept a string of length 1. The module-level function `IS_CHARACTER_JUNK` may be used to filter out whitespace characters (a blank or tab; **note**: bad idea to include newline in this!). Use of IS_CHARACTER_JUNK is recommended. """ self.linejunk = linejunk self.charjunk = charjunk def compare(self, a, b): r""" Compare two sequences of lines; generate the resulting delta. Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the `readlines()` method of file-like objects. The delta generated also consists of newline- terminated strings, ready to be printed as-is via the writelines() method of a file-like object. Example: >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True), ... 'ore\ntree\nemu\n'.splitlines(True))), ... end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu """ cruncher = SequenceMatcher(self.linejunk, a, b) for tag, alo, ahi, blo, bhi in cruncher.get_opcodes(): if tag == 'replace': g = self._fancy_replace(a, alo, ahi, b, blo, bhi) elif tag == 'delete': g = self._dump('-', a, alo, ahi) elif tag == 'insert': g = self._dump('+', b, blo, bhi) elif tag == 'equal': g = self._dump(' ', a, alo, ahi) else: raise ValueError('unknown tag %r' % (tag,)) yield from g def _dump(self, tag, x, lo, hi): """Generate comparison results for a same-tagged range.""" for i in range(lo, hi): yield '%s %s' % (tag, x[i]) def _plain_replace(self, a, alo, ahi, b, blo, bhi): assert alo < ahi and blo < bhi # dump the shorter block first -- reduces the burden on short-term # memory if the blocks are of very different sizes if bhi - blo < ahi - alo: first = self._dump('+', b, blo, bhi) second = self._dump('-', a, alo, ahi) else: first = self._dump('-', a, alo, ahi) second = self._dump('+', b, blo, bhi) for g in first, second: yield from g def _fancy_replace(self, a, alo, ahi, b, blo, bhi): r""" When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ... ['abcdefGhijkl\n'], 0, 1) >>> print(''.join(results), end="") - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ """ # Don't synch up unless the lines have a similarity score above # cutoff. Previously only the smallest pair was handled here, # and if there are many pairs with the best ratio, recursion # could grow very deep, and runtime cubic. See: # https://github.com/python/cpython/issues/119105 # # Later, more pathological cases prompted removing recursion # entirely. cutoff = 0.74999 cruncher = SequenceMatcher(self.charjunk) crqr = cruncher.real_quick_ratio cqr = cruncher.quick_ratio cr = cruncher.ratio WINDOW = 10 best_i = best_j = None dump_i, dump_j = alo, blo # smallest indices not yet resolved for j in range(blo, bhi): cruncher.set_seq2(b[j]) # Search the corresponding i's within WINDOW for rhe highest # ratio greater than `cutoff`. aequiv = alo + (j - blo) arange = range(max(aequiv - WINDOW, dump_i), min(aequiv + WINDOW + 1, ahi)) if not arange: # likely exit if `a` is shorter than `b` break best_ratio = cutoff for i in arange: cruncher.set_seq1(a[i]) # Ordering by cheapest to most expensive ratio is very # valuable, most often getting out early. if (crqr() > best_ratio and cqr() > best_ratio and cr() > best_ratio): best_i, best_j, best_ratio = i, j, cr() if best_i is None: # found nothing to synch on yet - move to next j continue # pump out straight replace from before this synch pair yield from self._fancy_helper(a, dump_i, best_i, b, dump_j, best_j) # do intraline marking on the synch pair aelt, belt = a[best_i], b[best_j] if aelt != belt: # pump out a '-', '?', '+', '?' quad for the synched lines atags = btags = "" cruncher.set_seqs(aelt, belt) for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes(): la, lb = ai2 - ai1, bj2 - bj1 if tag == 'replace': atags += '^' * la btags += '^' * lb elif tag == 'delete': atags += '-' * la elif tag == 'insert': btags += '+' * lb elif tag == 'equal': atags += ' ' * la btags += ' ' * lb else: raise ValueError('unknown tag %r' % (tag,)) yield from self._qformat(aelt, belt, atags, btags) else: # the synch pair is identical yield ' ' + aelt dump_i, dump_j = best_i + 1, best_j + 1 best_i = best_j = None # pump out straight replace from after the last synch pair yield from self._fancy_helper(a, dump_i, ahi, b, dump_j, bhi) def _fancy_helper(self, a, alo, ahi, b, blo, bhi): g = [] if alo < ahi: if blo < bhi: g = self._plain_replace(a, alo, ahi, b, blo, bhi) else: g = self._dump('-', a, alo, ahi) elif blo < bhi: g = self._dump('+', b, blo, bhi) yield from g def _qformat(self, aline, bline, atags, btags): r""" Format "?" output and deal with tabs. Example: >>> d = Differ() >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n', ... ' ^ ^ ^ ', ' ^ ^ ^ ') >>> for line in results: print(repr(line)) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \tabcdefGhijkl\n' '? \t ^ ^ ^\n' """ atags = _keep_original_ws(aline, atags).rstrip() btags = _keep_original_ws(bline, btags).rstrip() yield "- " + aline if atags: yield f"? {atags}\n" yield "+ " + bline if btags: yield f"? {btags}\n" # With respect to junk, an earlier version of ndiff simply refused to # *start* a match with a junk element. The result was cases like this: # before: private Thread currentThread; # after: private volatile Thread currentThread; # If you consider whitespace to be junk, the longest contiguous match # not starting with junk is "e Thread currentThread". So ndiff reported # that "e volatil" was inserted between the 't' and the 'e' in "private". # While an accurate view, to people that's absurd. The current version # looks for matching blocks that are entirely junk-free, then extends the # longest one of those as far as possible but only with matching junk. # So now "currentThread" is matched, then extended to suck up the # preceding blank; then "private" is matched, and extended to suck up the # following blank; then "Thread" is matched; and finally ndiff reports # that "volatile " was inserted before "Thread". The only quibble # remaining is that perhaps it was really the case that " volatile" # was inserted after "private". I can live with that . def IS_LINE_JUNK(line, pat=None): r""" Return True for ignorable line: if `line` is blank or contains a single '#'. Examples: >>> IS_LINE_JUNK('\n') True >>> IS_LINE_JUNK(' # \n') True >>> IS_LINE_JUNK('hello\n') False """ if pat is None: # Default: match '#' or the empty string return line.strip() in '#' # Previous versions used the undocumented parameter 'pat' as a # match function. Retain this behaviour for compatibility. return pat(line) is not None def IS_CHARACTER_JUNK(ch, ws=" \t"): r""" Return True for ignorable character: iff `ch` is a space or tab. Examples: >>> IS_CHARACTER_JUNK(' ') True >>> IS_CHARACTER_JUNK('\t') True >>> IS_CHARACTER_JUNK('\n') False >>> IS_CHARACTER_JUNK('x') False """ return ch in ws ######################################################################## ### Unified Diff ######################################################################## def _format_range_unified(start, stop): 'Convert range to the "ed" format' # Per the diff spec at http://www.unix.org/single_unix_specification/ beginning = start + 1 # lines start numbering with one length = stop - start if length == 1: return '{}'.format(beginning) if not length: beginning -= 1 # empty ranges begin at line just before the range return '{},{}'.format(beginning, length) def unified_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n'): r""" Compare two sequences of lines; generate the delta as a unified diff. Unified diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with ---, +++, or @@) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The unidiff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. Example: >>> for line in unified_diff('one two three four'.split(), ... 'zero one tree four'.split(), 'Original', 'Current', ... '2005-01-26 23:30:50', '2010-04-02 10:20:52', ... lineterm=''): ... print(line) # doctest: +NORMALIZE_WHITESPACE --- Original 2005-01-26 23:30:50 +++ Current 2010-04-02 10:20:52 @@ -1,4 +1,4 @@ +zero one -two -three +tree four """ _check_types(a, b, fromfile, tofile, fromfiledate, tofiledate, lineterm) started = False for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n): if not started: started = True fromdate = '\t{}'.format(fromfiledate) if fromfiledate else '' todate = '\t{}'.format(tofiledate) if tofiledate else '' yield '--- {}{}{}'.format(fromfile, fromdate, lineterm) yield '+++ {}{}{}'.format(tofile, todate, lineterm) first, last = group[0], group[-1] file1_range = _format_range_unified(first[1], last[2]) file2_range = _format_range_unified(first[3], last[4]) yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm) for tag, i1, i2, j1, j2 in group: if tag == 'equal': for line in a[i1:i2]: yield ' ' + line continue if tag in {'replace', 'delete'}: for line in a[i1:i2]: yield '-' + line if tag in {'replace', 'insert'}: for line in b[j1:j2]: yield '+' + line ######################################################################## ### Context Diff ######################################################################## def _format_range_context(start, stop): 'Convert range to the "ed" format' # Per the diff spec at http://www.unix.org/single_unix_specification/ beginning = start + 1 # lines start numbering with one length = stop - start if not length: beginning -= 1 # empty ranges begin at line just before the range if length context: yield None, None, None lines_to_write = context else: lines_to_write = index index = 0 while(lines_to_write): i = index % context index += 1 yield contextLines[i] lines_to_write -= 1 # Now yield the context lines after the change lines_to_write = context-1 try: while(lines_to_write): from_line, to_line, found_diff = next(line_pair_iterator) # If another change within the context, extend the context if found_diff: lines_to_write = context-1 else: lines_to_write -= 1 yield from_line, to_line, found_diff except StopIteration: # Catch exception from next() and return normally return _file_template = """ %(table)s%(legend)s """ _styles = """ :root {color-scheme: light dark} table.diff {font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace; border:medium} .diff_header {background-color:#e0e0e0} td.diff_header {text-align:right} .diff_next {background-color:#c0c0c0} .diff_add {background-color:palegreen} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa} @media (prefers-color-scheme: dark) { .diff_header {background-color:#666} .diff_next {background-color:#393939} .diff_add {background-color:darkgreen} .diff_chg {background-color:#847415} .diff_sub {background-color:darkred} }""" _table_template = """ %(header_row)s %(data_rows)s """ _legend = """

Legends
Colors  Added  Changed Deleted	Links (f)irst change (n)ext change (t)op

""" class HtmlDiff(object): """For producing HTML side by side comparison with change highlights. This class can be used to create an HTML table (or a complete HTML file containing the table) showing a side by side, line by line comparison of text with inter-line and intra-line change highlights. The table can be generated in either full or contextual difference mode. The following methods are provided for HTML generation: make_table -- generates HTML for a single side by side table make_file -- generates complete HTML file with a single side by side table See tools/scripts/diff.py for an example usage of this class. """ _file_template = _file_template _styles = _styles _table_template = _table_template _legend = _legend _default_prefix = 0 def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None, charjunk=IS_CHARACTER_JUNK): """HtmlDiff instance initializer Arguments: tabsize -- tab stop spacing, defaults to 8. wrapcolumn -- column number where lines are broken and wrapped, defaults to None where lines are not wrapped. linejunk,charjunk -- keyword arguments passed into ndiff() (used by HtmlDiff() to generate the side by side HTML differences). See ndiff() documentation for argument default values and descriptions. """ self._tabsize = tabsize self._wrapcolumn = wrapcolumn self._linejunk = linejunk self._charjunk = charjunk def make_file(self, fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5, *, charset='utf-8'): """Returns HTML file of side by side comparison with change highlights Arguments: fromlines -- list of "from" lines tolines -- list of "to" lines fromdesc -- "from" file column header string todesc -- "to" file column header string context -- set to True for contextual differences (defaults to False which shows full differences). numlines -- number of context lines. When context is set True, controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the "next" link anchors before the next change (so click of "next" link jumps to just before the change). charset -- charset of the HTML document """ return (self._file_template % dict( styles=self._styles, legend=self._legend, table=self.make_table(fromlines, tolines, fromdesc, todesc, context=context, numlines=numlines), charset=charset )).encode(charset, 'xmlcharrefreplace').decode(charset) def _tab_newline_replace(self,fromlines,tolines): """Returns from/to line lists with tabs expanded and newlines removed. Instead of tab characters being replaced by the number of spaces needed to fill in to the next tab stop, this function will fill the space with tab characters. This is done so that the difference algorithms can identify changes in a file when tabs are replaced by spaces and vice versa. At the end of the HTML generation, the tab characters will be replaced with a nonbreakable space. """ def expand_tabs(line): # hide real spaces line = line.replace(' ','\0') # expand tabs into spaces line = line.expandtabs(self._tabsize) # replace spaces from expanded tabs back into tab characters # (we'll replace them with markup after we do differencing) line = line.replace(' ','\t') return line.replace('\0',' ').rstrip('\n') fromlines = [expand_tabs(line) for line in fromlines] tolines = [expand_tabs(line) for line in tolines] return fromlines,tolines def _split_line(self,data_list,line_num,text): """Builds list of text lines by splitting text lines at wrap point This function will determine if the input text line needs to be wrapped (split) into separate lines. If so, the first wrap point will be determined and the first line appended to the output text line list. This function is used recursively to handle the second part of the split line to further split it. """ # if blank line or context separator, just add it to the output list if not line_num: data_list.append((line_num,text)) return # if line text doesn't need wrapping, just add it to the output list size = len(text) max = self._wrapcolumn if (size